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The entire LoC consists of a microfluidics part for the sample preparation and hybridization, a microsystem part including the application specific array of sensors for the electronic de

Volume 2008, Article ID 520641, 11 pages

doi:10.1155/2008/520641

Research Article

Microarchitecture of a MultiCore SoC for Data Analysis

of a Lab-on-Chip Microarray

G Kornaros 1, 2 and S Blionas 2

Correspondence should be addressed to G Kornaros,kornaros@gmail.com

Received 30 November 2007; Revised 21 May 2008; Accepted 24 June 2008

Recommended by P.-C Chung

This paper presents a reconfigurable architecture of a lab-on-chip (LoC) microarray device capable to process data either in genotyping or in gene expression applications in a fraction of the time that is required by the usual software methods running on

a standard computer The entire LoC consists of a microfluidics part for the sample preparation and hybridization, a microsystem part including the application specific array of sensors for the electronic detection, and finally a reconfigurable processing part for the data analysis The proposed data processing and analysis electronic module are an embedded multicore reconfigurable system-on-chip designed to analyze data from the forthcoming high-density oligonucleotide microarrays The proposed architecture employs reconfigurable technology and has the capacity to process data from microarrays of various sizes from small size ones used in genotyping up to large-scale gene expression arrays Additionally, the embedded processing cores feature reconfigurable circuitry for implementing the intense part of the processing, supplementing the various computational needs of the diverse applications for microarray real-time data processing and for a scalable reconfigurable architecture to handle also the future high-density microarrays

Copyright © 2008 G Kornaros and S Blionas This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Microarrays are a significant part of the lab-on-chip (LoC)

research area and are dedicated either for the parallel

assessment of gene expressionfor hundreds or thousands of

genes in a single experiment,or for genotyping molecular

diagnostics applications and particularly for

pharmacoge-nomics Despite the wideemployment of microarrays in

molecular biology and genetics, technical problems still exist,

for example, identifying and recognizing reliable data using

image processing techniques Currently, the microarray data

analysis is done with offline photographic methods, and

further quality assessment of the data, after segmentation

spot/background, grid matching, and noise suppression [1,

2], follows These further data processing steps require a

larger number of data to be stored, particularly when the

number of spots on the microarray is of several thousands

(gene expression applications), and then to process them for

the quality assessment Those issues so far did not allow the

full integration and operation of the LoC microarrays either

as standalone devices for possible consumer applications

(e.g., self-tests), or as intelligent systems creating much less data for further processing [3] Electronic hybridization detection allows high integration level of LoCs but the reading of the array of the sensors and the further data processing in case of large microarrays (the number of the sensors on the microarray with embedded electronic detection capability may reach nowadays several thousands), and also the on-chip processing of microarrays, still remain

an open research issue [4 7]

This paper presents the architecture of the electronic part of a fully integrated robust biomedical, biodiagnos-tics electronic microsystem This architecture processes the measurements of the electronic hybridization detec-tion sensors and hosted at a disposable device-cartridge which first extracts the DNA from a blood drop, then, it amplifies the fragmented tiny DNA samples (using PCR) and finally runs biological protocols which evaluate the analyzed substance It can be encapsulated in a single, portable, self-contained device-unit, significantly reducing the risks of cross-contamination inherent in conventional analysis methods An array of embedded sensors monitors

the hybridization of the sample with the biological material

put on the microarray spots The LoC is controlled by the

proposed architecture that monitors and adjusts the process

of the data produced by the electronic hybridization

detec-tion Subsequently, it executes an automated methodology to

flexibly execute normalization, transformation, and removal

of unreliable spot raw data The proposed architecture due

to its modularity is capable to further process data ranging

from small microarrays (few hundreds of spots) up to

large multithousands of spots microarrays producing vast

amount of data and evaluate the final results for molecular

diagnostics examinations

Targeted application areas are mutation detection for

gene expression, genotyping, and pharmacogenomics

Addi-tionally, the proposed architecture of the LoC device could be

used also for prediction, prevention, and even early diagnosis

or predisposition of specific diseases Molecular diagnosis of

infectious diseases, virus molecular detection, and so forth

are also possible applications Forthcoming genetic tests not

only will be dedicated for diagnostic of diseases but also

for personalized medicine treatment (pharmacogenetics)

They will also provide information to optimize drug therapy

increasing the efficiency and minimizing the adverse effects

of the developed drugs

the microfluidics sample preparation module of the LoC,

and of the electronic hybridization detection presenting

two different approaches, the photonic sensors alternative

and the capacitive sensors one.Section 3presents the data

processing algorithms that are required to analyze the

electronic hybridization detection sensor data and decide

about the existence of the examined mutations.Section 4is

describing two alternative architectures for the data analysis

of LoC data Finally, Section 5 is presenting an emulation

of these architectures as well as performance evaluation

results, andSection 6shows the processing of the data of a

microarray with 8500 spots

2 STATE OF THE ART FOR THE LAB-ON-CHIP

The lab-on-chip consists of subsystems for the sample

preparation for the electronic hybridization detection and

the data analysis The sample preparation subsystems in

case of DNA analysis concerns DNA extraction, PCR, and

hybridization (microfluidics subsystem) Then, electronic

hybridization detection subsystem concerns the

measure-ment of hybridization “degree” using dedicated biosensors

and their associated reading circuitry Finally, data analysis is

the targeted subsystem by this paper and concerns the flow of

data from the reading circuitry to the data analysis subsystem

and the processing of data by this Below, there are presented

indicatively state of the art implementation approaches for

the first two subsystems that are not the target areas of

this paper, namely, the sample preparation and electronic

hybridization detection subsystems

DNA extraction and amplification are usually

prerequi-site steps that are needed so as to provide a sufficient number

of copies of the target gene sequences to enable visualization

using specific detection modules, and thus identification

or characterization of gene sequences Conventional genetic analysis in clinical laboratories typically requires bench-top equipment and either manual or robotic transfer of liquids (e.g., 10–500 uL) between tubes (or microwells in the case of microliter plates) for separate steps of the process Using conventional approaches, DNA extraction is most commonly implemented by initially rupturing the cells (cell lysis) in a buffer solution (e.g., a solution including SDS), then capture of the released DNA with either silica particles

in a filter-type format, or silica-coated paramagnetic beads which can then be immobilized with a magnet This allows all other cellular debris to be washed away, after which this “template” DNA can be eluted from the beads and resuspended in a liquid buffer ready for amplification using the polymerase chain reaction (PCR) PCR involves cycling the DNA through a series of temperatures using a programmable thermal cycler Initially, the two strands of the template DNA duplex are separated by denaturation

at ∼95◦C, then short synthetic DNA “primer” sequences are annealed to the ends of the target section of template sequence (i.e., at a temperature usually between 50–60◦C), from which the Taq polymerase enzyme “zips” together the nucleotides present in the reaction mixture to build a new DNA sequence complimentary to template By cycling the reaction through this process, usually between 25 and 40 times, the number of available copies of DNA increases exponentially, so as to yield a sufficient of DNA enabling detection and analysis DNA analysis will continue either to identify specific sequences in specific parts of the DNA or

to compare the expression of genes of various samples and extract results for the role of genes in specific diseases Microfluidics parts of LoCs usually implement these sample preparation steps fully automatically The early years usually were made by silicon and glass Microfluidics technology has made great strides in recent years [8 10] Nowadays a trend toward polymers as substrate material hasbeen observed ([11], for review see Zhang et al [12]) Plastic substrates are less expensive and easier to manipulate

in mass production than silica-based substrates Advances in polymer engineering have led recently to the development

of a biochip device consisting of a plastic microfluidic chip,

a printed circuit board (PCB), and a Motorola eSensor microarray chip The plastic chip includes a mixing unit for rare cell capture using immunomagnetic separation, a cell preconcentration/purification/lysis/PCR unit, and a DNA microarray chamber

The developed LoC uses composite materials based mainly on plastic foils (especially PDMS) and different types of fibers (especially silicon carbide fibers) A modular technology for the micrfluidics part of the biochip is under development in the Tyndall National Institute A similar technology, without the use of metallized fibers, is also reported in the literature [11]

Concerning the hybridization chamber that hosts the electronic detection part of the LoC, it was designed considering various constraints and functions The main chamber that accommodates the hybridization of the sample DNA with the biological material of the spots is as small as possible; it allows the measurement of all biological spots

with pitch 300μm centre to centre and 170 μm diameter

spots A heater controls and stabilizes the temperature

The chamber is isolated from external light and has the

smallest auto fluorescence as possible The packaging of the

chamber onto the sensors is predicted to be easy for the

final assembly of the device Probes grafting is performed

before the chamber assembly If optical detection is employed

then alignment between biological spots and sensors is

mandatory, while in the case of the capacitive sensors this

is not required The whole system is compact and is designed

for optimized volumes (capillaries, hybridization chamber,

tanks) Finally, it is of low consumption, robust, while at the

same time ensures waterproofness

Several protocols for microarray-based SNP and

muta-tion analysis have been developed (as reviewed in [13])

There is the tiled arrays approach [14] that allows a variety

of electronic detection techniques Tiled arrays involve the

generation of an array of oligos that vary in specific positions

in order to create perfect matches to the fragmented DNA

molecules which will bind strongly or mismatches that will

result in weaker binding

In the photodetection context, tiled oligonucleotide

arrays are suitable for single color detection [14] The

fragmented DNA molecules are labeled with a fluorophore

probe, and the more or less binding pairs result in relative

intensities of the oligo spots that have to be compared This

requires the same amount of functional oligo to be deposited

(by spotting) or synthesized [15] at each spot The aim

is to minimize variations in the amount of arrayed oligo,

which will impede the analysis of single color intensities The

optical setup for the detection includes an excitation light

source, typically LEDs or a laser, optical filters to separate the

excitation light wavelength for the fluorescence wavelength,

and a detector There is a range of microarray scanners

available for scanning and detection of DNA

microarray-based platforms The lowest cost and least sensitive is a

CCD- (or CMOS-) based imaging system, where the whole

microarray is illuminated with the excitation light source,

and image processing is used to determine the results

Alternatively, a laser scanning-based microarray scanner can

be used In this configuration, a laser beam is raster scanned

across the microarray device The fluorescence is collected

via appropriate optics and filters into a photomultiplier tube

(PMT) Also a 2D array of photon counting sensors on a

single chip could enable detection of images of fluorescent

hybridized DNA samples It utilizes the high speed operation

and low light level detection capability of the 2nd generation

silicon detectors, the Geiger Mode-APD [16] These devices

produced using CMOS compatible processing are low power

as appropriate for POC and portable applications and will

have a low-cost base

MEMS sensors are based on mechanical movements

and deformations of their micromachined components,

such as single-clamped suspended beams (cantilevers),

double-clamped suspended beams (bridges), or suspended

diaphragms

In capacitive detection, the displacement is measured as

a change in the capacitance of a plane capacitor.An approach

for the detector array based on the stress induced on a thin

Contacts

Si membrane

SiO2 Cavity

Substrate Protein acceptor

(a)

Protein

(b) Figure 1: Hybridization process using capacitive sensors

silicon membrane due to reactions between the receptor DNA deposited on the membrane surface and the sample under investigation will be explored This kind of detectors has been successfully applied in biological applications employing silicon cantilevers and optical or piezoresistive detection Capacitive detection could challenge the sensitiv-ity and flexibilsensitiv-ity achieved by both of these techniques Capacitive DNA sensors arrays based on the exploitation

of surface stress changes and subsequent bending of an ultra silicon thin membrane are to be fabricated The membrane will seal the capacitor plates from the electrolyte solution thus enabling capacitive detection

In this array, each element of the array will be a capacitor comprised of an ultra thin silicon membrane suspended over

a cavity and a counter electrode on the substrate Operation

of the device will rely on the induced stress due to the reaction between the receptor DNA, a number of ultra thin silicon membranes covering a shallow cavity formed into a silicon dioxide layer etched on a silicon substrate containing the counter electrode of the capacitor detector InFigure 1, the basic idea is illustrated The hybridization process (b) results in membrane deflection due to the change of the surface free energy that eliminates the need for attaching labels to detect specific binding Special provision will be taken so that the device accommodates for the microfluidics

to be incorporated on the system

Statistical analysis of microarray data can essentially process massive amounts of data and can also adjust for various sources of variability in order to identify the important genes

or existing mutations amongst a large number which are interrogated This section summarizes some of the issues involved and provides a brief review of the processing algorithm mostly used by the researchers and will be accommodated by the proposed architecture

All microarray LoC experiments involve a number of distinct steps The design of an experiment involves the following:

(i) the number and the type of the genes’ mutations to

be interrogated,

(ii) for each of the above mutations of a gene, the exact

sequence of bases named oligos should be printed on

the LoC,

(iii) the design of appropriate sources of RNA to be

hybridized, and

(iv) the number of replicates for each of the oligos on the

LoC for increased statistical confidence

After hybridization that completes the data acquisition from

the LoC sensors takes place, next several data filtering

steps must follow The data must be processed to acquire

mutant and wild values; these are represented as red and

green in traditional microarrays In addition, the background

intensities should be estimated so as to correct the mutant

and wild values The aim is to adjust for sensor-bias and

for any systematic variation other than that due to the

differences between the RNA samples being examined Then,

the corrected values are further analyzed to decide about the

existence of a mutation in a sample or to select differentially

expressed (DE) genes or to find groups of genes whose

expression profiles can reliably classify the different RNA

sources into meaningful groups The discussion in this

section corresponds roughly to these data analysis steps

The following notation will be used throughout this

section The mutant and wild sensor measurements are

denoted as M f and W f for each spot The background

intensity will be I b Having estimated the background

intensity, it is almost universal practice to correct the mean

values of the measuredM fandW f intensities by subtracting

the mean value of the background,M = M f − I bandW =

W f − I b These adjusted intensities form the primary data for

all subsequent analyses

The motivation for background adjustment is that

a spot’s measured intensity includes a contribution not

specifically due to the hybridization of the target to the probe

For example, nonspecific hybridization may occur and/or

fluorescence may be emitted from other chemicals on the

detection part of the LoC (in the case of photosensor-based

hybridization detection) If such a contribution is present, we

would like to measure and remove it so as to obtain a more

accurate quantification of the actual hybridization Research

has begun as discussed in [17] on more sophisticated

methods of background adjustment which will produce

positive adjusted intensities even when the background

estimate happens to be larger than the foreground Empirical

experience suggests that local background estimates often

overestimate the true background while the morphological

method may underestimate it, and these differences have

a marked impact on the M-values for less intense spots.

There is a need for further research on adaptive background

correction methodologies which can produce intensities

with consistent behavior regardless of background estimator

method used

The data produced by the developed LoC after

hybridiza-tion are processed to infer if specific mutahybridiza-tions are present in

the examined sample and consequently to decide on what is

the appropriate medicine cluster that a patient should get

Assuming thatN replicas have been chosen, the microarray

is partitioned toN subarrays that correspond to N groups of

sensors Each subarray is spotted with the wild-type probes and with the mutant probes There are also spots of an oligo that will never hybridize in order to be used as control and background reference; these are the nonbinding control probes The calculations to be carried out on the data for

a mutation for both the wild and the mutant spots are summarized by the steps of the following algorithm

Step 1 calculation of the mean values for the wild, the

mutant and the nonbinding control spots:

N

N



i =1

whereN is the number of replicas for each probe.

Step 2 calculation of standard deviation (SD) for the wild,

the mutant and the nonbinding control spots:





1

N

N



i =1



x i − X2

Step 3 the coefficient of variation is then calculated and expressed as a percentage:

CV%=

 Standard Deviation Mean

If the calculated CV% is below 60 (as studied in [11]) then jump to Step5

If the calculated CV% is over 60 then continue to Step4

Step 4 (Calculation of new mean values excluding the

outliers) Assuming that we would like to keep our mea-surements within the 95% confidence interval, then this defines a distance of D = 1.96 ∗SD, where we will keep our measurements All measurements outside this region (MeanValue± D) will be considered as outliers and they will

be excluded Calculation of new mean values is excluding the outliers This final mean value of the reference group is memorized to be used in the next step for all the other groups

of the LoC

Step 5 (Background correction) Sources of variation in

the microarray such as unequal quantities of starting RNA

or differences in hybridization conditions across the array usually affect expression intensities It is therefore required the task of correction of microarray data so that to determine more meaningful and accurate biological data This is referred to as background correction The final values for wild and mutant probes are calculated by subtracting the background mean value I bfrom the calculated mean value after the outliers step (for both wild and mutant), so as to result with the final hybridization detection value of a probe

Step 6 (Decision about the existence of an SNP) The

calculations produce the ratio of the mutant and the wild mean values (M/W) According to the research results in

[11], if this ratio is greater than 2 then the specific mutation

is considered as existing

An alternative approach is to use the log-differential

expression ratio This is expressed as log2(M/W) =log2M −

log2W for each spot It is convenient to use base-2 logarithms

for the ratioM/W so that M is units of 2-fold change On this

scale,M =0 represents equal expression,M =1 represents

a 2-fold change between the RNA samples,M =2 represents

a 4-fold change, and so on Hence, in case of using log values

then the threshold is the value 1

Other statistical approaches commonly used to improve

significance estimates are a penalized t-test and a

Z-test using intensity-dependent variance estimates; these are

assuming that photographic methods are used to extract

the hybridization results, but also apply to our

capacitive-based microarray However, as shown in [17], the major

shortcoming of the t-statistic is that the replicate ratios

can occasionally be extremely similar due to randomness,

producing thus an artificially low standard deviationand

high t values False positives stemming from this effect

prevent the standardt-statistic from serving as a reliable or

useful test of which genes are truly regulated

The above steps are repeated for all the interrogated

mutations in the LoC, and according to the predefined

rules the cluster where a patient belongs is defined and an

indication on the disposable LoC informs the consumer

(patient), about this decision

4 SYSTEM-ON-CHIP ARCHITECTURE FOR

MICROARRAY DATA ANALYSIS

We describe and evaluate in detail the two alternative

architectures of the single-core and multicore approach

Also, the details for the data analysis of the microarray of a

custom Lab-on-Chip are described

4.1 Microarray data analysis on a single core CPU

with accelerator

The reading process of the sensors’ values is the first

step before the data analysis part; this reading requires a

conversion of the indication of the analog sensor to a digital

value Depending on the sensor type, two options exist for

the analog to digital conversion In case of the photosensors

traditional A/D, converters are used and their parallel output

is forwarded to the data bus to be transferred to the

appropriate processing core for further data analysis In case

of the capacitive sensors, capacitance measurement is carried

out by measuring frequency; an interface reader (IFRD)

is a simple circuitry converting the capacitance changes to

digital pulses and subsequently to an arithmetic value if

the microarray is capacitive-based The conversion requires

about 1 microsecond for each measurement as discussed in

[18], and it allows for a frequency of reading up to 1 MHz

Using one reading circuitry, it will need a total of 1 second

for 106spots These data are forwarded to the processing core

in the case of single core architecture

As a first approach, the entire microarray could be read

and monitored by a single core microprocessor, yet simple

and energy efficient in order to comply with the

require-ments of a cheap, portable, and flexible microsystem for

Sensor

controller

I/F

RD

MicroBlaze

SRAM Flash Figure 2: Single core data analysis architecture

pharmacogenomic applications This microprogrammed-based system offers itself for easy update of the algorithms in firmware; these algorithms perform the data processing while

at the same time do not cause excessive processing delay However, high-throughput microarrays with thousands of spots, for achieving real-time performance (less than 1 second waiting for getting the result) will obviously require more processing power as it will be shown at the next section

In the genomics area, the biologists need to compare the expression level of thousands of genes in the same time using at the same time many of these high-end microarrays

in parallel In the next part of the section, we present a reconfigurable system-on-chip with the capacity to handle such applications in real time

The organization of the LoC microsystem board is depicted inFigure 2 The device is controlled by the firmware loaded in single core microprocessor (MicroBlaze operating

at 100 MHz) This same core will be responsible to provide a user interface and postprocessing the analysis results via the

μBlaze CPU core The embedded microprocessor executes

the feature extraction algorithm to decide in which category the patient under analysis belongs to

In order to evaluate and design a scalable architecture

to elaborate large volume of DNA microarray data, we used field-programmable gate array (FPGA) technology The first target of evaluation is the use of a hardware acceleration unit to perform the computation intensive processing parts

We implemented a single core MicroBlaze-based system on FPGA which executes the processing algorithm depicted

pure hardware accelerator to perform the core algorithmic functions Regarding the resources in FPGA, the MicroBlaze cost is 730 slices, while a hardware block to calculate the

SD result is 155 slices of a Virtex-4 XC4VFX12-FF668-10C device

the calculations of the standard deviations (i.e., a square root operation) are completed for all mutations on the microarray Obviously, it is very beneficial to adopt a hard-ware accelerator unit since the performance is considerably improved

Nevertheless, manipulating data from larger-scale microarrays will ask for more increased processing power Hence, in addition to hardware acceleration there are needed more efficient solutions mostly based on multiple processing cores to achieve real-time operation

In the nextSection 4.2, the architecture of a multicore system is described to meet the processing requirements

of data analysis forreal-time operation for the current microarray defining also a scalable architecture for real-time operation for future higher-end microarrays

External host

· · ·

· · ·

· · ·

· · ·

· · ·

· · ·

Value Value CoreID CoreID GeneID GeneID

S

.

OPB arb.

I/F

Rd 0

Rd 1

I/F

RdK

S

M

S

M M

BRAM

IFRD arbiter synchronizer

BRAM controller micro Blaze 0

BRAM controller Micro Blaze 0

BRAM controller hostI/F

Accel erator

Accel erator

BRAM

BRAM LMB

LMB

Figure 3: Organization of the multicore microarray data processing SoC; normalization and statistical estimation are performed in parallel

in the MicroBlaze cores assisted by harware accelerators

Table 1: Implementation results: calculation of standard deviation

for the sensor data with and without hardware accelerator

Reading time of entire array 8.25 milliseconds

Standard deviation calculation for one

muta-tion (μBlaze at 100 MHz) 6600 microseconds

Standard deviation calculation for the entire

array (μBlaze at 100 MHz, calculating 850

probes)

5600 milliseconds

Standard deviation calculation for one

muta-tion (using a HW core-accelerator) 0.36 microseconds

Standard deviation calculation for the entire

array (using a HW core-accelerator) 3.06 milliseconds

4.2 A multicore reconfigurable architecture for

microarray data processing

Multithousand sensor microarrays for gene expression

anal-ysis produce large volume of data that necessitate the

employment of a scalable processing microarchitecture and

adjustment of the quality control algorithm of Section 3

for parallel processing The critical components of

prepro-cessing are identified, evaluated, and accelerated in order

to minimize the processing time and assure real-time

operation.Figure 3shows the organization of the proposed

reconfigurable architecture

Considering the processing core frequency that reaches

100 MHZ and using 10 reading circuitries and pipelining of

the measurements allow for a reading frequency of 10 MHz

These data are distributed by the IFRD Arbiter synchroniser

to the appropriate core for the data processing

Each interface reader (IFRD) block is assigned to a set

of lines of the microarray If the sensors are CCD-based or

photosensors, then multiple analog-to-digital converters can

be used instead to the left part of the IFRD with negligible

changes to the right part, which is interfacing to the data processing farm AssumingK IFRD blocks and N processors,

a simple interconnection bus-based scheme is employed in order to build a low complexity system; this allows each IFRD block to send the retrieved values to the appropriate

processor The IFRD Arbiter is responsible to initiate and

synchronize the transfers The protocol supported by the Arbiter is crucial for the efficient management of data transfers and triggering of the processing phases; it defines the following system parameters

(i) The FIFOs in effect in each IFRD block that are needed to maintain temporary read raw values (ii) The timing of transfer-events: the IFRD Arbiter triggers the reading process in a wave like fashion

in order to avoid conflicts over the shared bus An additional reason is that the order of completion of the processing is known in advance and thus the results are expected to arrive in the shared RAM in order

However, in order to accelerate the processing, a more relaxed approach is adopted: the retrieval of data from the IFRD blocks is not enforced on a strict time window basis This is also facilitated by the principle of operation of the LoC Different IFRD blocks may have to send the retrieved values to the same processor, since these belong to the same

“gene” (replicas of it for the statistical processing) This methodology is used in each large-scale microarray in order

to obtain more accurate results by placing the same biological material on different locations so as to avoid microarray area variability side effects In addition, the computations for the mean values calculations for each of the genes may start just

as the first two replicas’ values for each gene arrive to the local BRAM of each MicroBlaze core

The IFRD Arbiter is also in this case responsible to

synchronize the transfers The Arbiter acts as a Master and

bram_block

PORTB PORTA mb_bram

SLMB BRAM BRAM SLMB lmb_bram_if_cntlr lmb_bram_if_cntlr dlmb_cntlr ilmb_cntlr

bram_block

PORTB PORTA SLMB BRAM BRAM SLMB

PORTB PORTA

PORTB PORTA BRAM BRAM SLMB lmb_bram_if_cntlr lmb_bram_if_cntlr

bram_block_0

bram_block bram_block_1

bram_block bram_block_2

dlmb_cntlr_1

lmb_bram_if_cntlr dlmb_cntlr_3

lmb_bram_if_cntlr dlmb_cntlr_5 dlmb_cntlr_0

lmb_bram_if_cntlr dlmb_cntlr_2

lmb_bram_if_cntlr dlmb_cntlr_4 PROCESSOR

PROCESSOR

PROCESSOR ilmb

microblaze microblaze_0

microblaze microblaze_1

microblaze microblaze_2 microblaze_3

microblaze

DLMB ILMB DPLB IPLB

DLMB

DLMB ILMB

ILMB

DLMB ILMB DPLB IPLB

mb_plb SLAVES OF mb_plb

SPLB

SPLB

xps_gpio xps_uartlite xps_mch_emc

LEDs_4Bit RS232_Uart SRAM

xps_timer

xps_timer_0

debug_module_MBDEBUG_0

mdm debug_module MBDE SPLB

DEBUG

lmb_v10_0 lmb_v10_3 lmb_v10_1

lmb_v10_2

lmb_v10_4 lmb_v10_5

Figure 4: The ML405 board hosting a Virtex4-FX20 1 MB SRAM and 128 MB DDR memory; the 4-MicroBlaze system-on-FPGA is responsible to perform the microarray data analysis algorithm in parallel The accelerator blocks were added directly in the netlist last

triggers the transfer of each ready value to the correct

MicroBlaze This is also the reason why eventually a small

FIFO maybe will be required at each RFID block to store the

intermediate read values Each value read from a sensor at

(x, y) coordinates is considered as the body of a packet send

to a specific processor with a “coreID” identifier—which is

the destination This core handles all the values of the replicas

of this gene with the same “geneID” identifier—which stands

for the source field of the packet Each processor handles a

number of genes and does not need to wait until all the values

arrive It is obvious that the processing of the mean value

starts as soon as there are data in the local BRAM

The processing in this first phase consists of the sensor

data processing algorithm The simple operations, additions,

and subtractions are performed in software while the more

complex ones by the hardware accelerator that resides on

the second port of the local BRAM This accelerator block

shown inFigure 3is able to calculate a square root function

When a value is placed at address sq addr source, then the

accelerator is triggered and 11 clock cycles later (with a clock

cycle time of 10 nanoseconds) the result is placed at address

sq addr result At the same time the MicroBlaze has already

erased the content of sq addr source and then waits for the

outcome to appear

After the SD result of a gene is computed a

third-processing level starts, which aims to identify the outliers that

discard them and recalculate normalized mean values In a

fully constrained relaxed system (without the IFRD Arbiter

to cause artificial delay), this phase causes the OPB bus to

operate at full throughput However, this final phase does not last long compared to the rest of the processing

THE SINGLE AND MULTICORE RECONFIGURABLE ARCHITECTURES

The implementation of the system-on-chip (SoC) of both alternative architectures (single and multicore) using, respec-tively, one and four MicroBlaze CPUs is done in a XC4VFX20-FF668 FPGA using the ML405 prototype board from Xilinx (see Figure 4) In the case of the multicore alternative, each MicroBlaze CPU is responsible to handle the processing of 106 (425/4) mutations retrieved from 10 subarrays (we implemented 10 replicas) of 10×85 spots An on-chip timer is triggered when we initiate the calculations until each step completes; thus real-time measurements at clock cycle granularity were achieved The on-board SRAM is used as a shared memory among all MicroBlazes to exchange messages and to store the final normalized data

In order to evaluate the performance benefits against the additional cost in resources, we used a single core, so

as to examine the existence of only one single mutation The entire algorithm ofSection 2is executed using floating point representation of values (without compromising on the accuracy of the results), with and without hardware acceleration

Using floating point values in the algorithm increases the accuracy but at the cost of increased execution time Thus,

2

4

6

8

10

12

14

16

18

×10 5

MicroBlaze (u32)

MicroBlaze (float)

MicroBlaze with FP unit (float) MicroBlaze with FP unit, barrel shifter, integer divider (float) Figure 5: Execution time of the algorithm on a MicroBlaze with

different configurations of the MicroBlaze core

enabling the option of using the hardware acceleration units

(floating point, barrel shifter, and integer divider unit) of the

MicroBlaze CPU is a challenging alternative.Figure 5shows

that using the embedded hardware floating-point units of

the MicroBlaze core gives a boost of almost 3-fold speedup;

if the rest hardware units of MicroBlaze are also enabled

then, asFigure 5depicts, does not payoff in the scope of this

application

Moreover, employing hardware accelerator for the square

root and the division operations improves significantly the

performance Figure 6compares the cost in clock cycles of

executing the entire algorithm of Section 2 using a single

core, so as to examine the existence of one single mutation

using floating point representation (without compromising

on the accuracy of the results), with and without hardware

acceleration The plot shows the breakdown of the execution

time for each group of steps according toSection 3 Step2

performs the standard deviation calculation that costs 660 K

clock cycles It is obvious that the lack of use of the hardware

acceleration part has the counter effect of increased runtime

Hence, it is advantageous to use the hardware core to

calcu-late the square root as discussed also in the previous section;

adopting this core gives a total time of 19 K clock cycles

The Virtex4-FX20 device allowed us to implement an

SoC with four MicroBlaze cores Given that we have the

list of retrieved values arriving in the local memory of each

CPU, the next step is to run the algorithm for each of

the interrogated mutations It must be noted that many

mutations are manipulated by the same CPU Figure 7

depicts the execution time from the single MicroBlaze system

0 1 2 3 4 5 6 7

×10 5

Without acceleration With acceleration s6

s3, s4, s5

s2 s1 Figure 6: Comparison of the cost in clock cycles of executing the entire algorithm (Steps1–6) ofSection 2for one mutation using floating point representation (we decided not to compromise on the accuracy of the results), with and without hardware acceleration

0 2 4 6 8 10 12 14 16 18

×10 6

Single core 4-core SoC s6

s3, s4, s5

s2 s1 Figure 7: Performance of data processing on a single and a four-core system-on-FPGA

and the fully parallel multicore system using four cores The overhead due to communication is negligible leading to significant improvement of the total running time

and of the individual components that are critical for performance and the on-chip resources The system designer can determine the right option to enable during the design and development according to the requirements, balancing cost of silicon area versus processing time Currently, the system-on-FPGA has the capacity to run the described algorithm for 425 mutations with 10 replicas each in less than

400 milliseconds

After the execution of the algorithm is completed for every interrogated mutation then the extracted results must

be further analyzed to be shown to the user, either for genotyping analysis, or for gene expression The architectural option made was to utilize the on-board SRAM with a PLB

Table 2: Implementation results, area resources and performance

of a 4-MicroBlaze SoC and analysis breakdown to the critical blocks;

if longer processing times are affordable a less costly solution can be

a 4-MicroBlaze SoC without a floating-point unit

SoC components in a

Clock cycle (nanosecond)

1 Microblaze, 1 Ilmb,

1 Dlmb controller, 1 lmb

32 KB

1 MicroBlaze FP unit extra

MicroBlaze configuration

System with No FP-Unit 6238 (73%) 64 (94%) 9.9

System with 4 FP-enabled

MicroBlaze 8350 (98%) 64 (94%) 9.9

interface controller; one MicroBlaze acting as a Master to be

responsible to transfer the results of the processed data to an

external host for further use and visualization

MICROARRAY BY THE SYSTEM-ON-FPGA

The proposedmulticore architecturewas prototyped on

anF-PGA platform (Virtex-4-FX20) and was used to process data

from a glass slide microarray The microarray featured 8500

spots for 425 mutations variations and their associated wild

type with 10 replicas for each of them This microarray

was designed by the Genomics Lab of the Welcome Trust

of Oxford University The probes on the microarraywere

designed to investigate the following factors to determine

their effect on the accuracy of oligonucleotide arrays:

(i) isotherm versus nonisothermal probe design,

(ii) oligonucleotide probe length,

(iii) position of mismatch,

(iv) influence of different types of DNA variation (size

of deletion or insertion and nature of

substitution-mismatched base pairs do not have equal stability),

(v) analysis of both strands,

(vi) length of linker,

(vii) use of control probes

In order to investigate all these parameters and select

the most efficient design of probes for each of 20 selected

mutations, a large number of probes were required; therefore

the microarray format selected for use was a custom array

The total number of individual mutations examined was

20 and a total of 425 variations The specific names of the

mutations and of the disease that they are related with are

not disclosed here due to ongoing patenting process Thus,

we will use here numbers 1–20 as name and to keep a track

of them MUT will stand for mutation and WT for their

associated wild type For each mutation are printed two kind

Table 3: The list of the mutations used in the microarray for getting the data for the performance evaluation

#=1–20,∗ =1–14 maximum #=1–20,∗ =1–14 maximum

#-MUTA-I-Pm-∗ #-WTA-I-Pm-∗

#-MUTS-I-Pm-∗ #-WTS-I-Pm-∗

#-MUTA-I2-Pm-∗ #-WTA-I2-Pm-∗

#-MUTS-I2-Pm-∗ #-WTS-I2-Pm-∗

of probes on the microarray, the probe sequence that binds to the antisense strand and the probe sequence that binds to the sense strand (MUTA and MUTS, resp., with their associated WTA and WTS)

A dedicated software program of Oxford University was used allowing varying parameters so that isothermal probes can be designed, with different lengths, and with the position

of the mismatch varying around the centre position by a desired distance

For each sequence, the probes were designed following the isothermal approach (I stands for isothermal), using a 15-mer linker (oligo comprised of 15 bases), within a 5-degree window (70–75◦C in the dedicated software program) and also following a lower isothermal (63–68◦C) so to test the effect of this (I2 stands for the lower isothermal) Pm probes stand for 25-mer Affymetrix style probes Thus, the following variants ofeach mutation are interrogated in the designed microarray shown inTable 3

The custom array is being fabricated by Oxford Gene Technologies (OGT) The arrays are fabricated using in situ oligonucleotide synthesis by an ink-jet printing method The 8.5 K array from OGT uses a hybridization chamber with

8500 oligos in it

The microarray was clustered in subarrays of 10×85 spots Each such subarray gives 425 total variations of 20 mutations with their associated wild types A total of 425 probes were hosted in this array along with their associated wild type, including the positive and nonbinding control sequences These are used to identify faulty hybridization cases and to define the background correction value Each

of these probes has 10-spot replicas The algorithm processes

10 measurements for the mutant probe and another 10 for the associated wild type

of the performance of the proposed architecture Even if hybridization took place only with Cy3 labelled target, the array was scanned with both channels (red and green), and then only the green channel was analyzed The picture was taken by an Agilent scanner

Oxford has carried out hybridizations using a normal DNA control (without the mutation) and DNAs heterozy-gous or homozyheterozy-gous for the mutation The produced scanner data was processed by the proposed emulated

Figure 8: The custom microarray used for getting the data of the

performance evaluation

0

2

4

6

8

10

12

14

16

×10 2

Probe name

3-MUTA-I3-WTA-I 3-MUTA-I & 3-WTA-I

3-MUTA-I

3-WTA-I

Figure 9: Data analysis results of mutation 3 (antisense strand), by

the emulated architecture for wild type hybridization

architecture on the FPGA executing the algorithmof

probes to interrogate the studied mutations

The hybridization performance of a particular probe

was compared between the wild type hybridization and the

mutant hybridization The ratio of the intensity change for

that probe was then compared to all the other probes for the

same allele present on the array

The Mutation-3 case (antisense strand) data analysis

results for the normal DNA control hybridization is shown

The Mutation-3 case (sense strand) data analysis results

are shown inFigure 10

From the calculations of the algorithm, antisense wild

type variation 11 and mutant type 8 (3-WTA-I-11,

3-MUTA-0 2 4 6 8 10 12 14 16

×10 2

Probe name

3-MUTS-I3-WTS-I 3-MUTS-I & 3-WTS-I

3-MUTS-I 3-WTS-I Figure 10: Data analysis results of mutation 3 (sense strand), by the emulated architecture for wild type hybridization

I-9) were selected as the most appropriate probes to detect mutation 3

The data analysis (of normalized values) was carried out using the proposed architecture and allowed the selection of

a number of probe pairs suitable for the detection of 18 of the 20 mutations The total time required to produce these results was 0.4 seconds

These hybridizations have provided Oxford with a sig-nificant amount of data and hopefully it will allow them to substantially decrease the number of probes to be tested in the future

When scaling to multithousand sensor microarrays, the data volume increases significantly along with the pro-cessing time The data analysis of the results retrieved from microarrays requires processing power and is a time-consuming, cumbersome, and often error-prone task A data processing algorithm that was presented is capable to analyze the electronic hybridization detection sensor data of

a LoC and to decide about the existence of the interrogated mutations Two alternative architectures for the data analysis were emulatied and their performance was evaluated Data taken from an implemented microarray of 8.500 spots was processed by the emulated architecture, and the results are presented

The presented architecture is a robust data analysis cir-cuitry for a Lab-on-Chip, which provides increased reliability

by automating spot detection and data processing by on-chip dedicated highly integrated hardware In particular, the proposed data processing and analysis electronic module are

an embedded multicore reconfigurable scalable system-on-chip architecture which is capable to process in a fraction of nowadays processing time data of the current microarrays but also of the future multithousand sensor microarrays